Efficacy of dupilumab and risk factors for dupilumab-induced hypereosinophilia in severe asthma: a preliminary study from China.

OBJECTIVE: Dupilumab has been approved for the treatment of severe asthma with type 2 inflammation by inhibiting interleukin (IL)-4 and IL-13 signaling. However, dupilumab-induced hypereosinophilia (HE) has been reported and should not be ignored. The aim of this study was to investigate the efficacy of dupilumab in Chinese patients with severe asthma, whether HE affects its efficacy, and the possible risk factors for HE. METHODS: 20 patients with severe asthma who received dupilumab treatment for at least 12 months in the First Affiliated Hospital of Guangzhou Medical University from 2019 to 2022 were included. We compared clinical data and laboratory tests results before dupilumab treatment and at 4 and 12 months after treatment. Based on whether dupilumab treatment triggers HE defined as blood eosinophil count (BEC) ≥ 1.5 × 109 cells/L, the patients were allocated into non-HE and HE groups. RESULTS: The patients showed a significant increase in asthma control test (ACT) scores, a decrease in the number of exacerbations, a decrease in the proportion of patients taking an oral corticosteroid (OCS) and in the dose, and a significant improvement in the pulmonary function parameters FEV1/FVC (%) and FEV1 (% predicted) after 4 and 12 months of treatment with dupilumab. For type 2 inflammatory biomarkers, the levels of fractional concentration of exhaled nitric oxide (FeNO), sputum eosinophil count percentage (SEC%) and total immunoglobulin E (TIgE) decreased significantly, whereas BEC were higher after 4 months of treatment, but returned to baseline levels after 12 months. 8 patients (40%) developed asymptomatic HE after dupilumab, and the efficacy was not significantly different between the HE and non-HE groups. The earliest BEC elevation appeared at 1 month after treatment, but most of them declined after 6 months, and basically returned to the baseline level around 12 months of treatment. In addition, we further found that when patients had FeNO ≥ 60 ppb, food allergens positive and combined eosinophilic otitis media (EOM), their BEC increased significantly more than that of the control group after 4 months as well as 12 months of treatment. CONCLUSIONS: This study demonstrated that dupilumab was efficacious in Chinese patients with severe asthma, and some patients developed asymptomatic, self-limited HE, which did not affect its efficacy. Additionally, FeNO ≥60 ppb, food allergens positive, and co-morbidities with EOM may be the risk factors for developing HE.

Serum Proteomic Analysis Identifies SAA1, FGA, SAP, and CETP as New Biomarkers for Eosinophilic Granulomatosis With Polyangiitis.

Background: Eosinophilic granulomatosis with polyangiitis (EGPA) is characterized by asthma-like attacks in its early stage, which is easily misdiagnosed as severe asthma. Therefore, new biomarkers for the early diagnosis of EGPA are needed, especially for differentiating the diagnosis of asthma. Objectives: To identify serum biomarkers that can be used for early diagnosis of EGPA and to distinguish EGPA from severe asthma. Method: Data-independent acquisition (DIA) analysis was performed to identify 45 healthy controls (HC), severe asthma (S-A), and EGPA patients in a cohort to screen biomarkers for early diagnosis of EGPA and to differentiate asthma diagnosis. Subsequently, parallel reaction monitoring (PRM) analysis was applied to a validation cohort of 71 HC, S-A, and EGPA patients. Result: Four candidate biomarkers were identified from DIA and PRM analysis-i.e., serum amyloid A1 (SAA1), fibrinogen-α (FGA), and serum amyloid P component (SAP)-and were upregulated in the EGPA group, while cholesteryl ester transfer protein (CETP) was downregulated in the EGPA group compared with the S-A group. Receiver operating characteristics analysis shows that, as biomarkers for early diagnosis of EGPA, the combination of SAA1, FGA, and SAP has an area under the curve (AUC) of 0.947, a sensitivity of 82.35%, and a specificity of 100%. The combination of SAA1, FGA, SAP, and CETP as biomarkers for differential diagnosis of asthma had an AUC of 0.921, a sensitivity of 78.13%, and a specificity of 100%, which were all larger than single markers. Moreover, SAA1, FGA, and SAP were positively and CETP was negatively correlated with eosinophil count. Conclusion: DIA-PRM combined analysis screened and validated four previously unexplored but potentially useful biomarkers for early diagnosis of EGPA and differential diagnosis of asthma.

Eosinophilia and Lung Cancer: Analysis From Real-World Data and Mendelian Randomization Study.

Background and Objective: Growing evidence added to the results from observational studies of lung cancer patients exhibiting eosinophilia. However, whether eosinophils contributed to tumor immune surveillance or neoplastic evolution was unknown. This study aimed to analyze the causal association between eosinophilia and lung cancer. Methods: The causal effect of eosinophil count on lung cancer from a genome-wide association study (GWAS) was investigated using the two-sample Mendelian randomization (MR) method. Secondary results according to different histological subtypes of lung cancer were also implemented. Meanwhile, we compared the measured levels of blood eosinophil counts among different subtypes of lung cancer from real-world data. Results: The median absolute eosinophilic count (unit: 109/L) [median (min, max): Lung adenocarcinoma 0.7 (0.5, 15); Squamous cell lung cancer 0.7 (0.5, 1.3); Small cell lung cancer 0.7 (0.6, 1.3); p = 0.96] and the median eosinophil to leukocyte ratio [median (min, max): Lung adenocarcinoma 8.7% (2.1, 42.2%); Squamous cell lung cancer 9.3% (4.1, 17.7%); Small cell lung cancer 8.9% (5.1, 24.1%); p = 0.91] were similar among different histological subtypes of lung cancer. MR methods indicated that eosinophilia may provide 28% higher risk for squamous cell lung cancer in East Asian [Weighted median method: odds ratio (OR) = 1.28, 95% CI: 1.04-1.57, p = 0.02]. Conclusion: Our study suggested that eosinophilia may be a potential causal risk factor in the progression of squamous cell lung cancer in East Asian.

Lonicerin attenuates house dust mite-induced eosinophilic asthma through targeting Src/EGFR signaling.

Eosinophilic asthma is the predominant phenotype of asthma, and although these patients are sensitive to glucocorticoid therapy, they also experience many side effects. Lonicerin is a kind of bioflavonoid isolated from the Chinese herb Lonicera japonica Thunb, which has anti-inflammatory and immunomodulatory effects. The aim of this study was to elucidate the effects of lonicerin on eosinophilic asthma and its potential mechanisms. Here, we established a house dust mite (house dust mite)-induced eosinophilic asthma model in BALB/c mouse, and evaluated the effects of lonicerin on it. Our results showed that lonicerin significantly reduced airway hyperresponsiveness the number of inflammatory cells (especially eosinophils) and the elevation of interleukin (IL)-4, IL-5, IL-13 and eotaxin in bronchoalveolar lavage fluid (BALF) supernatants of mice. Additionally, lonicerin also eminently blunted inflammatory infiltration and mucus secretion, as well as mRNA levels of Mucin 5AC (MUC5AC) in lung tissue. Furthermore, results of network pharmacology and molecular docking revealed that Src kinase and epidermal growth factor receptor may be the potential targets responsible for the effects of lonicerin. Finally, in vivo experiments confirmed that lonicerin inhibited activation of the Src/EGFR pathway by decreasing their phosphorylation. Taken together, the present study demonstrated that lonicerin could suppress HDM-induced eosinophilic asthma in mice through inhibiting the activation of Src/EGFR pathway, which also provides a basis for further research as a new potentially therapeutic agent for eosinophilic asthma and its underlying mechanisms in the future.

TSSK4 upregulation in alveolar epithelial type-II cells facilitates pulmonary fibrosis through HSP90-AKT signaling restriction and AT-II apoptosis.

Alveolar epithelial injury is one of the important pathological changes in idiopathic pulmonary interstitial fibrosis (IPF), but the regulatory mechanism remains unclear. Here, we reported that alveolar epithelial type-II cells (AT II) play important roles in pathological process of pulmonary fibrosis. Through iTRAQ (isobaric tagging for relative and absolute quantification) quantitative proteomics, TSSK4 was identified to be upregulated in bleomycin-induced fibrotic mice model, which was further confirmed in clinical IPF patients' tissue specimens. TSSK4 is a germ-related protein, but its expression in other tissues and the association with other diseases are not reported. Immunofluorescence staining showed that TSSK4 selectively expressed in AT-II cells, which are essential for inflammation-induced AT-II loss during fibrosis. Luciferase assay and other molecular biological experiments proved that TSSK4 expression is regulated by TNF-α-mediated NF-κB signaling. The TSSK4 kinase activity is found to be closely related to the function of HSP90-AKT pathway that TSSK4 can phosphorylate its substrate HSP90ß on serine 255, to inhibit the ATPase activity of HSP90ß and reduce its molecular chaperone function on AKT. Under this condition, kinase activity of AKT is diminished to interfere its survival function, subsequently facilitating AT-II cellular apoptosis through the mitochondrial death machinery. Our findings highlight the importance of TSSK4 in regulating pulmonary fibrosis by facilitating AT-II loss through HSP90-AKT signaling, all of which suggest TSSK4 and the regulating mechanism as attractive targets for the clinical intervention of pulmonary injury and fibrosis.

CT Quantification of COVID-19 Pneumonia at Admission Can Predict Progression to Critical Illness: A Retrospective Multicenter Cohort Study.

Objective: Early identification of coronavirus disease 2019 (COVID-19) patients with worse outcomes may benefit clinical management of patients. We aimed to quantify pneumonia findings on CT at admission to predict progression to critical illness in COVID-19 patients. Methods: This retrospective study included laboratory-confirmed adult patients with COVID-19. All patients underwent a thin-section chest computed tomography (CT) scans showing evidence of pneumonia. CT images with severe moving artifacts were excluded from analysis. Patients' clinical and laboratory data were collected from medical records. Three quantitative CT features of pneumonia lesions were automatically calculated using a care.ai Intelligent Multi-disciplinary Imaging Diagnosis Platform Intelligent Evaluation System of Chest CT for COVID-19, denoting the percentage of pneumonia volume (PPV), ground-glass opacity volume (PGV), and consolidation volume (PCV). According to Chinese COVID-19 guidelines (trial version 7), patients were divided into noncritical and critical groups. Critical illness was defined as a composite of admission to the intensive care unit, respiratory failure requiring mechanical ventilation, shock, or death. The performance of PPV, PGV, and PCV in discrimination of critical illness was assessed. The correlations between PPV and laboratory variables were assessed by Pearson correlation analysis. Results: A total of 140 patients were included, with mean age of 58.6 years, and 85 (60.7%) were male. Thirty-two (22.9%) patients were critical. Using a cutoff value of 22.6%, the PPV had the highest performance in predicting critical illness, with an area under the curve of 0.868, sensitivity of 81.3%, and specificity of 80.6%. The PPV had moderately positive correlation with neutrophil (%) (r = 0.535, p < 0.001), erythrocyte sedimentation rate (r = 0.567, p < 0.001), d-Dimer (r = 0.444, p < 0.001), high-sensitivity C-reactive protein (r = 0.495, p < 0.001), aspartate aminotransferase (r = 0.410, p < 0.001), lactate dehydrogenase (r = 0.644, p < 0.001), and urea nitrogen (r = 0.439, p < 0.001), whereas the PPV had moderately negative correlation with lymphocyte (%) (r = -0.535, p < 0.001). Conclusions: Pneumonia volume quantified on initial CT can non-invasively predict the progression to critical illness in advance, which serve as a prognostic marker of COVID-19.

Serum Cytokine Profiling Identifies Axl as a New Biomarker Candidate for Active Eosinophilic Granulomatosis With Polyangiitis.

Background: Eosinophilic granulomatosis with polyangiitis (EGPA) prognosis is generally favorable and is treated with combined corticosteroids/immunosuppressor(s) therapy. However, disease flares increase the number of clinical visits. Therefore, discovering new serum biomarkers for early identification of active EGPA is crucial. Objective: To identify reliable serum biomarkers to measure EGPA activity. Methods: The expression of 160 proteins was compared in sera from 15 inactive and 13 active EGPA patients by antibody-based microarray. Network-based analysis identified patterns in the different groups. Differentially expressed proteins (DEPs) in active disease were identified, and the correlation between their serum levels and clinical parameters was assessed. DEPs were further analyzed for GO enrichment and KEGG pathways. Finally, DEP marker candidates were validated by ELISA and Bio-plex as well as against a second cohort of 22 inactive and 18 active EGPA patients. Results: The active group presented higher peripheral and sputum eosinophil counts, FeNO, and FEV1 (% predicted) (P < 0.05). Network-based analysis showed scattered expression patterns in active subjects, but no significant bias in inactive subjects. Significant differences were observed in serum levels of 19 candidate markers, all of which were higher in active EGPA (P < 0.05). KEGG analysis indicated that DEPs were mainly involved in the MAPK, PI3K-Akt, RAS and Rap1 related pathways. Nine out of 19 candidate markers were positively correlated with peripheral eosinophil counts including FGF-7, SCF, GDNF, ß-NGF, IGFBP-4, Axl, PIGF, Insulin, NT-4, ErbB3, OPN and BMP-4 (r = 0.693, r = 0.692, r = 0.687, r = 0.683, r = 0.671, r = 0.606, r = 0.571, r = 0.570, r = 0.516, respectively; P < 0.05), while two, CD14 and MCP-3, were negatively correlated (r = -0.644 and r = -0.515; P < 0.05). The higher expression of Axl, OPN, HCC-4, GDNF, and MCP-3 in active EGPA subjects was confirmed by ELISA and Custom Multiplex Bio-plex analyses. Conclusion: The serum protein profiles were significantly different between active and inactive EGPA. The expression of the candidate proteins correlated with peripheral blood eosinophil count. Serum Axl, OPN, HCC-4, GDNF, and MCP-3 levels were consistently higher in active EGPA, independent of the assessment methods. Finally, Axl had the largest AUC, indicating that this cytokine may serve as novel biomarker for the diagnosis of active EGPA.

Machine learning models for predicting critical illness risk in hospitalized patients with COVID-19 pneumonia.

BACKGROUND: To develop machine learning classifiers at admission for predicting which patients with coronavirus disease 2019 (COVID-19) who will progress to critical illness. METHODS: A total of 158 patients with laboratory-confirmed COVID-19 admitted to three designated hospitals between December 31, 2019 and March 31, 2020 were retrospectively collected. 27 clinical and laboratory variables of COVID-19 patients were collected from the medical records. A total of 201 quantitative CT features of COVID-19 pneumonia were extracted by using an artificial intelligence software. The critically ill cases were defined according to the COVID-19 guidelines. The least absolute shrinkage and selection operator (LASSO) logistic regression was used to select the predictors of critical illness from clinical and radiological features, respectively. Accordingly, we developed clinical and radiological models using the following machine learning classifiers, including naive bayes (NB), linear regression (LR), random forest (RF), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), K-nearest neighbor (KNN), kernel support vector machine (k-SVM), and back propagation neural networks (BPNN). The combined model incorporating the selected clinical and radiological factors was also developed using the eight above-mentioned classifiers. The predictive efficiency of the models is validated using a 5-fold cross-validation method. The performance of the models was compared by the area under the receiver operating characteristic curve (AUC). RESULTS: The mean age of all patients was 58.9±13.9 years and 89 (56.3%) were males. 35 (22.2%) patients deteriorated to critical illness. After LASSO analysis, four clinical features including lymphocyte percentage, lactic dehydrogenase, neutrophil count, and D-dimer and four quantitative CT features were selected. The XGBoost-based clinical model yielded the highest AUC of 0.960 [95% confidence interval (CI): 0.913-1.000)]. The XGBoost-based radiological model achieved an AUC of 0.890 (95% CI: 0.757-1.000). However, the predictive efficacy of XGBoost-based combined model was very close to that of the XGBoost-based clinical model, with an AUC of 0.955 (95% CI: 0.906-1.000). CONCLUSIONS: A XGBoost-based based clinical model on admission might be used as an effective tool to identify patients at high risk of critical illness.

Severe Asthma with Viral Infection can Develop into Eosinophilic Granulomatosis with Polyangiitis.

Asthma is common in eosinophilic granulomatosis with polyangiitis (EGPA), and the annual incidence of EGPA in patients with asthma is much higher compared with the general population, and the trigger factor for this is unknown. We report a case of a 19-year-old male with a background of severe asthma who presented with eosinophilic lung infiltration after viral infection, which progressed to clinical EGPA. The diagnosis of EGPA was supported by an initial clinical presentation of recurrent cough and wheezing accompanied by a red rash, followed by peripheral eosinophilia, a high eosinophil percentage in bronchoalveolar lavage fluid (BALF), and migratory pulmonary eosinophilic infiltrates. Lung biopsy showed blood vessels with extravascular eosinophils. The patient responded well to high-dose glucocorticoids and cyclophosphamide, and symptoms and biochemical markers improved. Our literature review identified few reports on the triggers of EGPA, which highlights that viral infection may be a risk factor for asthma that progresses to EGPA.

Clinical symptoms, comorbidities and complications in severe and non-severe patients with COVID-19: A systematic review and meta-analysis without cases duplication.

BACKGROUND: The pandemic of COVID-19 poses a challenge to global healthcare. The mortality rates of severe cases range from 8.1% to 38%, and it is particularly important to identify risk factors that aggravate the disease. METHODS: We performed a systematic review of the literature with meta-analysis, using 7 databases to identify studies reporting on clinical characteristics, comorbidities and complications in severe and non-severe patients with COVID-19. All the observational studies were included. We performed a random or fixed effects model meta-analysis to calculate the pooled proportion and 95% confidence interval (CI). Measure of heterogeneity was estimated by Cochran's Q statistic, I index and P value. RESULTS: A total of 4881 cases from 25 studies related to COVID-19 were included. The most prevalent comorbidity was hypertension (severe: 33.4%, 95% CI: 25.4%-41.4%; non-severe 21.6%, 95% CI: 9.9%-33.3%), followed by diabetes (severe: 14.4%, 95% CI: 11.5%-17.3%; non-severe: 8.5%, 95% CI: 6.1%-11.0%). The prevalence of acute respiratory distress syndrome, acute kidney injury and shock were all higher in severe cases, with 41.1% (95% CI: 14.1%-68.2%), 16.4% (95% CI: 3.4%-29.5%) and 19.9% (95% CI: 5.5%-34.4%), rather than 3.0% (95% CI: 0.6%-5.5%), 2.2% (95% CI: 0.1%-4.2%) and 4.1% (95% CI: -4.8%-13.1%) in non-severe patients, respectively. The death rate was higher in severe cases (30.3%, 95% CI: 13.8%-46.8%) than non-severe cases (1.5%, 95% CI: 0.1%-2.8%). CONCLUSION: Hypertension, diabetes and cardiovascular diseases may be risk factors for severe COVID-19.

Potential therapeutic effects of dipyridamole in the severely ill patients with COVID-19.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can cause acute respiratory distress syndrome, hypercoagulability, hypertension, and multiorgan dysfunction. Effective antivirals with safe clinical profile are urgently needed to improve the overall prognosis. In an analysis of a randomly collected cohort of 124 patients with COVID-19, we found that hypercoagulability as indicated by elevated concentrations of D-dimers was associated with disease severity. By virtual screening of a U.S. FDA approved drug library, we identified an anticoagulation agent dipyridamole (DIP) in silico, which suppressed SARS-CoV-2 replication in vitro. In a proof-of-concept trial involving 31 patients with COVID-19, DIP supplementation was associated with significantly decreased concentrations of D-dimers (P < 0.05), increased lymphocyte and platelet recovery in the circulation, and markedly improved clinical outcomes in comparison to the control patients. In particular, all 8 of the DIP-treated severely ill patients showed remarkable improvement: 7 patients (87.5%) achieved clinical cure and were discharged from the hospitals while the remaining 1 patient (12.5%) was in clinical remission.